Surface-defect detecting method, surface-defect detecting apparatus, steel-material manufacturing method, steel-material quality management method, steel-material manufacturing facility, surface-defect determination model generating method, and surface-defect determination model

ABSTRACT

A surface-defect detecting method of optically detecting a surface defect of a steel material, the method including: an irradiation step of irradiating an examination target part with illumination light beams from different directions by using two or more distinguishable light sources; and a detection step of detecting a surface defect in the examination target part based on the degree of overlapping of bright portions extracted from two or more images formed by reflected light beams of the illumination light beams.

FIELD

The present invention relates to a surface-defect detecting method ofoptically detecting a surface defect of a steel material, asurface-defect detecting apparatus, a steel-material manufacturingmethod, a steel-material quality management method, a steel-materialmanufacturing facility, a surface-defect determination model generatingmethod, and a surface-defect determination model.

BACKGROUND

Recently, in the manufacturing process of an iron steel product, asurface defect of a steel material in hot rolling or cold rolling hasbeen required to be detected for yield improvement through prevention ofa large amount of non-conformance. A steel material in the presentdescription means an iron steel product such as a steel pipe (a seamlesssteel pipe, a welding steel pipe, or the like), a steel plate (ahot-rolled steel plate, a cold-rolled steel plate, a thick plate, or thelike), or a shaped steel, or a half-finished product (a slab or thelike) generated in a process through which the iron steel product ismanufactured. When a surface defect of a steel material is to bedetected through optical surface examination, it is sometimes difficultto distinguish a harmful concave-convex surface defect and a harmlessflat scale pattern (attributable to, for example, cooling unevenness)through simple image capturing of the steel material.

In a disclosed method of accurately distinguishing a concave-convexsurface defect and a flat scale pattern, distinguishable light beams areemitted from different directions, images are acquired by receivingreflected light of each light beam, and a subtraction image of theacquired images is obtained to detect a surface defect (refer to PatentLiterature 1). In another disclosed method, a bright-dark patterngenerated by the shape of a surface defect is recognized based on anobtained subtraction image, and whether the surface defect is concave orconvex is determined to further improve the accuracy of surface-defectdetection (refer to Patent Literature 2).

CITATION LIST Patent Literature

Patent Literature 1: Japanese Patent Application Laid-open No.2015-125089

Patent Literature 2: Japanese Patent Application Laid-open No.2015-210150

SUMMARY Technical Problem

With the methods disclosed in Patent Literatures 1 and 2, aconcave-convex surface defect and a flat scale pattern can be accuratelydistinguished based on a subtraction image. However, studies by theinventors of the present invention found that, in the methods disclosedin Patent Literatures 1 and 2, a signal of a base steel portion, whichhas almost no concave and convex portions and has a high surfacereflectance, after subtraction is similar to a signal of a concavesurface defect and thus difficult to distinguish in some cases. Notethat a base steel portion is generated due to flaking of a mill scale,which is caused by rubbing, collision, and the like mainly at atemperature lower than a temperature at which the mill scale isgenerated on the surface, and for example, a cold-rolling scratch is abase steel portion. A concave-convex surface defect is normallygenerated as a steel material is transferred with pressing of aprotrusion or the like baked on a roll while the steel material is softin hot rolling. Thus, the concave-convex surface defect has awell-defined concave-convex shape, and is oxidized like a sound portionthrough a cooling process and obtains a reflectance equivalent to thatof the sound portion. As a result, as illustrated in FIG. 22 (a), aclear bright-dark pattern is generated by light irradiation from oneside. However, a base steel portion is typically generated as a steelmaterial is rubbed with a structure such as a roll in cold rolling, andwhen a mill scale is flaked and base steel is exposed, the base steelportion obtains a reflectance higher than that of a sound portioncovered by the mill scale. As a result, as illustrated in FIG. 22 (b),the base steel portion has substantially no depth but has a highreflectance, and thus is captured as a high-luminance signalirrespective of the irradiation direction.

When the base steel portion slightly has concave and convex portions, abright-dark pattern similar to a concave-convex surface defect issometimes generated in a subtraction image acquired by applying themethods disclosed in Patent Literatures 1 and 2. The base steel portionis a surface defect that can be harmful or harmless depending on thepurpose of an examination, and causes no detection and excessivedetection of a surface defect. In particular, the concave-convex surfacedefect and the base steel portion are often treated as harmful andharmless, respectively, in an examination, such as thickness safeguard,for which depth is important, and it is desirable to accuratelydistinguish the concave-convex surface defect and the base steelportion.

The present invention is intended to solve the above-described problemand provide a surface-defect detecting method, a surface-defectdetecting apparatus, a surface-defect determination model generatingmethod, and a surface-defect determination model that are capable ofaccurately distinguishing a base steel portion and a surface defect. Thepresent invention is also intended to provide a steel-materialmanufacturing method, a steel-material quality management method, and asteel-material manufacturing facility that are capable of improving amanufacturing yield of a steel material by accurately distinguishing abase steel portion and a surface defect.

Solution to Problem

To solve the problem and achieve the object, a surface-defect detectingmethod of optically detecting a surface defect of a steel material,according to the present invention includes: an irradiation step ofirradiating an examination target part with illumination light beamsfrom different directions by using two or more distinguishable lightsources; and a detection step of detecting a surface defect in theexamination target part based on the degree of overlapping of brightportions extracted from two or more images formed by reflected lightbeams of the illumination light beams.

Moreover, in the surface-defect detecting method according to thepresent invention, the degree of overlapping of the bright portions is aratio at which an overlapping portion of the bright portions occupies asurface-defect candidate portion in the examination target part.

Moreover, in the surface-defect detecting method according to thepresent invention, the detection step includes a step of calculating asurface-defect candidate portion in the examination target part based onthe bright portions extracted from two or more images formed byreflected light beams of the illumination light beams, and a step ofdetecting a surface defect in the examination target part based on aratio at which an overlapping portion of the bright portions occupiesthe surface-defect candidate portion.

Moreover, in the surface-defect detecting method according to thepresent invention, the detection step includes a step of detecting asurface defect in the examination target part by using a surface-defectdetermination model subjected to machine learning such that adetermination value indicating whether or not the surface defect existsin the examination target part corresponding to the two or more imagesis output, when the degree of overlapping of bright portions extractedfrom the two or more images is input.

Moreover, a surface-defect detecting apparatus configured to opticallydetect a surface defect of a steel material, according to the presentinvention includes: an irradiation unit configured to irradiate anexamination target part with illumination light beams from differentdirections by using two or more distinguishable light sources; and adetection unit configured to detect a surface defect in the examinationtarget part based on the degree of overlapping of bright portionsextracted from two or more images formed by reflected light beams of theillumination light beams.

Moreover, a steel-material manufacturing method according to the presentinvention includes a step of manufacturing a steel material whiledetecting a surface defect of the steel material by using thesurface-defect detecting method according to the present invention.

Moreover, a steel-material quality management method according to thepresent invention includes a step of managing the quality of a steelmaterial by classifying the steel material based on existence of asurface defect by using the surface-defect detecting method according tothe present invention.

Moreover, a steel-material manufacturing facility according to thepresent invention includes: a manufacturing facility configured tomanufacture a steel material; and the surface-defect detecting apparatusaccording to the present invention that is configured to examine thesteel material manufactured by the manufacturing facility.

Moreover, a surface-defect determination model generating methodaccording to the present invention includes: using the degree ofoverlapping of bright portions extracted from two or more images formedby reflected light beams of illumination light beams with which anexamination target portion is irradiated from different directions byusing two or more distinguishable light sources and a result ofdetermination of whether or not a surface defect exists in theexamination target portion, as teacher data; and generating alearning-completed model by machine learning as a surface-defectdetermination model, where an input value of the learning-completedmodel being the degree of overlapping of bright portions extracted fromthe two or more images and an output value of the learning-completedmodel being a value of determination of whether or not a surface defectexists in the examination target portion corresponding to the two ormore images.

Moreover, a surface-defect determination model according to the presentinvention is the model generated by the surface-defect determinationmodel generating method according to the present invention.

Advantageous Effects of Invention

With a surface-defect detecting method, a surface-defect detectingapparatus, a surface-defect determination model generating method, and asurface-defect determination model according to the present invention,it is possible to accurately distinguish a base steel portion and asurface defect. With a steel-material manufacturing method, asteel-material quality management method, and a steel-materialmanufacturing facility according to the present invention, it ispossible to improve a manufacturing yield of a steel material byaccurately distinguishing a base steel portion and a surface defect.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram illustrating the configuration of asurface-defect detecting apparatus as an embodiment of the presentinvention.

FIG. 2 is a diagram illustrating exemplary subtraction images of a basesteel portion.

FIG. 3 is a diagram illustrating a result of AND processing performed ona bright-portion image of a concave surface defect.

FIG. 4 is a diagram illustrating a result of AND processing performed ona bright-portion image of a base steel portion.

FIG. 5 is a flowchart illustrating the process of a detection step in asurface-defect detecting method as a first embodiment of the presentinvention.

FIG. 6 is a flowchart illustrating the process of a detection step in asurface-defect detecting method as a second embodiment of the presentinvention.

FIG. 7 is a flowchart illustrating the process of a detection step in asurface-defect detecting method as a third embodiment of the presentinvention.

FIG. 8 is a diagram for describing exemplary labeling processingillustrated in FIG. 7.

FIG. 9 is a flowchart illustrating the process of a detection step in asurface-defect detecting method as a fourth embodiment of the presentinvention.

FIG. 10 is a diagram illustrating two exemplary two-dimensional imagesobtained through image capturing of a concave-convex surface defect andscale and/or harmless pattern, and an exemplary subtraction imagethereof.

FIG. 11 is a diagram illustrating shade and shadow when light isincident from one side in cases in which the surface shape of anexamination target part is concave and convex.

FIG. 12 is a diagram illustrating an exemplary subtraction image of aconcave surface defect.

FIG. 13 is a flowchart illustrating the process of a method ofcalculating the positional relation between a bright portion and a darkportion by using expansion processing.

FIG. 14 is a diagram for describing expansion-contraction processing.

FIG. 15 is a diagram illustrating an exemplary subtraction image and anexemplary one-dimensional profile of a bright-dark pattern.

FIG. 16 is a diagram illustrating an exemplary two-dimensional image ofa filter and an exemplary one-dimensional profile thereof.

FIG. 17 is a diagram illustrating an exemplary subtraction imagesubjected to filter processing by using the filter illustrated in FIG.16 and an exemplary one-dimensional profile thereof.

FIG. 18 is a diagram illustrating calculation results ofcombined-bright-portion occupancy histograms of a base steel portion anda concave-convex surface defect.

FIG. 19 is a flowchart illustrating the process of a detection step in asurface-defect detecting method as a fifth embodiment of the presentinvention.

FIG. 20 is a schematic diagram illustrating a modification of thedisposition positions of light sources.

FIG. 21 is a schematic diagram illustrating bright-dark patternsobtained with the disposition positions of the light sources illustratedin FIG. 20.

FIG. 22 is a schematic diagram for describing characteristics of aconcave-convex surface defect and a base steel portion.

DESCRIPTION OF EMBODIMENTS

The configuration and operation of a surface-defect detecting apparatusas an embodiment of the present invention will be described below withreference to the accompanying drawings.

[Configuration of Surface-Defect Detecting Apparatus]

FIG. 1 is a schematic diagram illustrating the configuration of asurface-defect detecting apparatus as an embodiment of the presentinvention. As illustrated in FIG. 1, this surface-defect detectingapparatus 1 as an embodiment of the present invention is a deviceconfigured to detect a surface defect of a cylindrical steel pipe Pconveyed in the illustrated arrow direction and includes, as maincomponents, light sources 2 a and 2 b, a function generator 3, areasensors 4 a and 4 b, an image processing device 5, and a monitor 6.

The light sources 2 a and 2 b are irradiation units. The light sources 2a and 2 b emit distinguishable illumination light beams L to a sameexamination target part on the surface of the steel pipe P in accordancewith a trigger signal from the function generator 3. The light sources 2a and 2 b are disposed symmetrically with respect to the examinationtarget part. Specifically, the light sources 2 a and 2 b are eachshifted by a same incident angle from the normal vector of the surfaceof the steel pipe P and disposed so that an irradiation direction vectorof the illumination light beams L and the normal vector of the surfaceof the steel pipe P are on a same plane.

The purpose of the same incident angle of the illumination light beams Lis to have same optical conditions as possible when light sources fromdifferent incident directions are distinguished. The incident angles areempirically regarded as same when the difference between the incidentangles is equal to or smaller than 20°. In addition, it is empiricallydesirable that the incident angle of each light source is 25° to 82.5°.It is desirable to select the incident angle in the range of 25° to 55°when the light quantity is desired to be increased, or in the range of60° to 82.5° when the light quantity is sufficient and the S/N ratio isdesired to be increased.

Note that each incident angle in the present specification means theangle between the incident direction of a light beam from the lightsource 2 a or 2 b and the normal of the surface of the examinationtarget part. The normal of the surface of the examination target part isset at 0°. Although the number of light sources is two in the presentembodiment, the number of light sources may be three or more as long asthey are distinguishable. Such distinguishable light sources mean lightsources for each of which the amount of reflected light beams obtainedfrom the examination target part can be separately calculated.

The area sensors 4 a and 4 b are image capturing units. The area sensors4 a and 4 b capture two-dimensional images formed by reflected lightbeams of the illumination light beams L emitted from the light sources 2a and 2 b in accordance with the trigger signal from the functiongenerator 3. The area sensors 4 a and 4 b input data of the capturedtwo-dimensional images to the image processing device 5. The areasensors 4 a and 4 b are installed on the normal vector of theexamination target part as closely as possible while image capturingvisual fields thereof are maintained. Note that although area sensorsare used in the present embodiment, line sensors may be used. In thiscase, captured images are one-dimensional images, but a surface-defectdetecting method to be described later is applicable.

The image processing device 5 is a detection unit. The image processingdevice 5 is a device configured to perform processing of determining aharmless base steel portion in the examination target part by using twotwo-dimensional images input from the area sensors 4 a and 4 b. Thisbase-steel-portion determination processing is the most importanttechnology of the present invention, and thus will be described later indetail. The image processing device 5 may perform defect determinationby machine learning. In addition, the image processing device 5 maydetect a surface defect in the examination target part as necessary byperforming subtraction processing to be described later. The imageprocessing device 5 outputs, to the monitor 6, two-dimensional imagesinput from the area sensors 4 a and 4 b and information related to aresult of surface-defect detection.

The surface-defect detecting apparatus 1 having such a configurationperforms the processing of determining a base steel portion in theexamination target part by executing the surface-defect detecting methoddescribed below. In addition, the surface-defect detecting apparatus 1distinguishes scale or harmless pattern, a concave-convex surfacedefect, and a base steel portion in the examination target part asnecessary. Note that scale or harmless pattern means portions includinga surface film or a surface texture having a thickness of several μm toseveral tens of μm approximately and optical properties different fromthose of the base steel portion, and portions that cause noise in thesurface-defect detecting method.

[Surface-Defect Detecting Method]

In surface-defect detecting methods disclosed in Patent Literatures 1and 2, the same subtraction processing on a two-dimensional image of abase steel portion of a cold-rolling scratch or the like generatesbright-dark patterns as illustrated in FIGS. 2 (b) to (e) like thebright-dark pattern of a concave-convex surface defect as illustrated inFIG. 2 (a) in some cases. This is because the base steel portion has ahigh reflectance and luminance variance and thus bright and darkportions are randomly generated. In the field of a steel material, suchas a thick plate or a steel pipe, for which the depth of a surfacedefect is important, in particular, the base steel portion extremelyfrequently occurs as compared to a surface defect, and thus is difficultto distinguish based on the characteristic amount or the like.

However, the base steel portion needs to be determined to be harmful orharmless, depending on examination needs. Thus, when the base steelportion having a bright-dark pattern similar to the bright-dark patternof the surface defect cannot be distinguished, excessive detectionoccurs and the performance of surface-defect detection decreases. Inaddition, the base steel portion needs to be distinguished as a surfacedefect in some cases when the base steel portion needs to be determinedto be harmful. Furthermore, the base steel portion has various shapesand needs to be determined to be harmless in many cases.

To stably detect such a base steel portion, its characteristic that thebase steel portion is brighter than a stationary portion when irradiatedwith illumination light beam from any direction can be used. Thestationary portion in the present description is a mill scale portion,which is not the surface defect nor the base steel portion.Specifically, the base steel portion can be detected by detecting anoverlapping portion of bright portions in the region of a surface-defectcandidate portion extracted through threshold value processing,labeling, or the like from two two-dimensional images input from thearea sensors 4 a and 4 b.

FIGS. 3 and 4 illustrate exemplary results of detecting, throughbinarization processing, bright portions of two two-dimensional imagesinput from the area sensors 4 a and 4 b and performing AND processing onimages of the detected bright portions for a concave surface defect anda base steel portion, respectively, at a surface-defect candidateportion. As illustrated in FIGS. 3 (c) to (e), the positions of thebright portions of the two two-dimensional images (FIGS. 3 (c) and (d))are different from each other for the concave surface defect, and thusno signals are not detected through the AND processing (FIG. 3 (e)).However, as illustrated in FIGS. 4 (c) to (e), the positions of thebright portions of the two two-dimensional images (FIGS. 4 (c) and (d))coincide with each other for the base steel portion, and thus signalsare detected at almost all places of bright portions and dark portionsthrough the AND processing (FIG. 4 (e)).

Note that a well-known method may be used as the method of detecting asurface-defect candidate portion at an examination target part. Inparticular, a method using a subtraction image, which is disclosed inPatent Literatures 1 and 2 has such an advantage that the method caneliminate a scale pattern and a harmless pattern through subtractionprocessing and use a unique bright-dark pattern generated by aconcave-convex surface defect, and thus can accurately detect asurface-defect candidate portion, in particular, a concave-convexsurface-defect candidate portion, and accordingly, the method ispreferable.

Note that the timing of detection of a surface-defect candidate portionmay be any timing as long as the detection step to be described later isperformed before the step (S5, S15, S25, S37, or S48 to be describedlater) of calculating a combined-surface-portion occupancy. For example,a surface-defect candidate portion may be detected in advance,separately from the detection step to be described later (refer to firstand second embodiments). Alternatively, for example, in the detectionstep to be described later, a surface-defect candidate portion may bedetected based on copies of a bright-portion binarized image “a” and abright-portion binarized image “b” used in the detection step (refer toa third embodiment).

Alternatively, for example, in parallel to the detection step to bedescribed later, a concave-convex surface defect may be detected byusing subtraction images obtained from copies of a raw image “a”, a rawimage “b”, a correction image “a”, a correction image “b”, and the likeused in the detection step, and the detected concave-convex surfacedefect may be set as a surface-defect candidate portion.

Thus, whether a surface-defect candidate portion is a surface defect ora base steel portion can be determined by extracting a combinedbright-portion image through AND processing on bright-portion images oftwo two-dimensional images and evaluating the surface-defect candidateportion based on the combined bright-portion image. A base steel portioncan be detected by extracting bright portions of the two two-dimensionalimages through threshold value processing and performing AND processingon the bright portions. In a case of a surface defect or the like aswell as a base steel portion, bright portions of two two-dimensionalimages obtained through irradiation from different directions slightlyoverlap by accident in some cases. However, in a case of a base steelportion, the reflectance is high in two two-dimensional images obtainedthrough irradiation from different directions, and bright portionsoverlap almost in the entire region. Thus, the degree of overlapping ofthe bright portions may be calculated as a characteristic amount, and abase steel portion and a surface defect may be distinguished by usingthis index. This method can more accurately perform distinguishment thanin a case in which determination is performed on the existence of anoverlapping portion, and thus is preferable.

The following describes surface-defect detecting methods as the first tofifth embodiments of the present invention, which are thought of basedon the above-described idea.

First Embodiment

First, a surface-defect detecting method as the first embodiment of thepresent invention will be described below with reference to FIG. 5.

The surface-defect detecting method as the first embodiment of thepresent invention includes an irradiation step, an image capturing step,and a detection step. In the irradiation step, the light sources 2 a and2 b emit distinguishable illumination light beams L to a sameexamination target part on the surface of the steel pipe P in accordancewith a trigger signal from the function generator 3. In the imagecapturing step, the area sensors 4 a and 4 b capture, in accordance witha trigger signal from the function generator 3, two-dimensional imagesformed by reflected light beams of the illumination light beams Lemitted from the light sources 2 a and 2 b. In the detection step, theimage processing device 5 determines a harmless base steel portion byusing the two two-dimensional images input from the area sensors 4 a and4 b.

FIG. 5 is a flowchart illustrating the process of the detection step inthe surface-defect detecting method as the first embodiment of thepresent invention. As illustrated in FIG. 5, first in the detection stepof the present embodiment, the image processing device 5 performs imagecorrection processing on two two-dimensional images (raw images “a” and“b”) input from the area sensors 4 a and 4 b, thereby generatingcorrection images “a” and “b” (steps S1 a and S1 b). The imagecorrection processing in the present description includes processing ofcutting out an examination target region, and processing such as shadingcorrection of correcting entire luminance unevenness attributable to anoptical system.

Subsequently, the image processing device 5 performs, on each of thecorrection images “a” and “b”, bright portion binarization processing ofdetecting a bright portion by setting one to the value of a pixel havinga luminance equal to or higher than a threshold value and setting zeroto the value of a pixel having a luminance lower than the thresholdvalue. Through this processing, the image processing device 5 generatesa bright-portion binarized image “a” and a bright-portion binarizedimage “b” (steps S2 a and S2 b). In FIG. 5, an exemplary bright-portionbinarized image “a” is illustrated to the left of the label“bright-portion binarized image “a””, and an exemplary bright-portionbinarized image “b” is illustrated to the right of the label“bright-portion binarized image “b””. Subsequently, the image processingdevice 5 performs AND processing on the bright-portion binarized images“a” and “b”. Through this processing, the image processing device 5extracts a pixel having the value of one in both bright-portionbinarized images “a” and “b”, thereby generating a combinedbright-portion image (step S3). In FIG. 5, an exemplary combinedbright-portion image is illustrated to the right of the label “combinedbright-portion image”.

Subsequently, the image processing device 5 performs masking calculationprocessing on the combined bright-portion image by using asurface-defect candidate portion. Through this processing, the imageprocessing device 5 generates a cut-out combined bright-portion imageobtained by cutting out a target region of determination as a base steelportion (in other words, a region that is a surface-defect candidateportion and a bright portion) (step S4). Subsequently, the imageprocessing device 5 calculates, as a combined-bright-portion occupancy,the ratio at which the cut-out combined bright-portion image occupiesthe entire surface-defect candidate portion (step S5). In addition, theimage processing device 5 determines whether the surface-defectcandidate portion is a base steel portion or a surface defect throughthreshold value processing or the like using the calculatedcombined-bright-portion occupancy. When having determined that thesurface-defect candidate portion is not a base steel portion, the imageprocessing device 5 determines that the surface-defect candidate portionis a surface defect (step S6). Note that the present embodiment is anexample in which the degree of overlapping of the combinedbright-portion image over the surface-defect candidate portion iscalculated, but another processing method is applicable as long as thedegree of overlapping of the combined bright-portion image over theentire surface-defect candidate portion can be calculated. For example,a surface-defect candidate portion may be measured by another methodsuch as eddy current flaw detection or ultrasonic wave flaw detection,and the ratio at which the combined bright-portion image occupies thesurface-defect candidate portion may be calculated.

Second Embodiment

Subsequently, a surface-defect detecting method as the second embodimentof the present invention will be described below with reference to FIG.6.

The surface-defect detecting method as the second embodiment of thepresent invention includes an irradiation step, an image capturing step,and a detection step. In the irradiation step, the light sources 2 a and2 b emit distinguishable illumination light beams L to a sameexamination target part on the surface of the steel pipe P in accordancewith a trigger signal from the function generator 3. In the imagecapturing step, the area sensors 4 a and 4 b capture, in accordance witha trigger signal from the function generator 3, two-dimensional imagesformed by reflected light beams of the illumination light beams Lemitted from the light sources 2 a and 2 b. In the detection step, theimage processing device 5 determines a harmless base steel portion byusing the two two-dimensional images input from the area sensors 4 a and4 b.

FIG. 6 is a flowchart illustrating the process of the detection step inthe surface-defect detecting method as the second embodiment of thepresent invention. As illustrated in FIG. 6, first in the detection stepof the present embodiment, the image processing device 5 performsmasking calculation processing using a surface-defect candidate portionon each of two two-dimensional images (raw images “a” and “b”) inputfrom the area sensors 4 a and 4 b, thereby generating a cut-out rawimage “a” and a cut-out raw image “b” by cutting out a target region ofdetermination as a base steel portion (steps S11 a and S11 b).Subsequently, the image processing device 5 performs image correctionprocessing on the cut-out raw images “a” and “b”, thereby generating acut-out correction image “a” and a cut-out correction image “b” (stepsS12 a and S12 b). The image correction processing in the presentdescription includes processing such as shading correction of correctingentire luminance unevenness attributable to an optical system.

Subsequently, the image processing device 5 performs, on each of thecut-out correction images “a” and “b”, bright portion binarizationprocessing of detecting a bright portion by setting one to the value ofa pixel having a luminance equal to or higher than a threshold value andsetting zero to the value of a pixel having a luminance lower than thethreshold value. Through this processing, the image processing device 5generates a cut-out bright-portion binarized image “a” and a cut-outbright-portion binarized image “b” (steps S13 a and S13 b).Subsequently, the image processing device 5 performs AND processing onthe cut-out bright-portion binarized images “a” and “b”. Through thisprocessing, the image processing device 5 extracts a pixel having thevalue of one in both cut-out bright-portion binarized images “a” and“b”, thereby generating a cut-out combined bright-portion image (stepS14). Subsequently, the image processing device 5 calculates, as acombined-bright-portion occupancy, the ratio at which the cut-outcombined bright-portion image occupies the entire surface-defectcandidate portion (step S15). In addition, the image processing device 5determines whether the surface-defect candidate portion is a base steelportion or a surface defect through threshold value processing or thelike using the calculated combined-bright-portion occupancy. When havingdetermined that the surface-defect candidate portion is not a base steelportion, the image processing device 5 determines that thesurface-defect candidate portion is a surface defect (step S16).

Third Embodiment

Subsequently, a surface-defect detecting method as the third embodimentof the present invention will be described below with reference to FIG.7.

The surface-defect detecting method as the third embodiment of thepresent invention includes an irradiation step, an image capturing step,and a detection step. In the irradiation step, the light sources 2 a and2 b emit distinguishable illumination light beams L to a sameexamination target part on the surface of the steel pipe P in accordancewith a trigger signal from the function generator 3. In the imagecapturing step, the area sensors 4 a and 4 b capture, in accordance witha trigger signal from the function generator 3, two-dimensional imagesformed by reflected light beams of the illumination light beams Lemitted from the light sources 2 a and 2 b. In the detection step, theimage processing device 5 determines a harmless base steel portion byusing the two two-dimensional images input from the area sensors 4 a and4 b.

FIG. 7 is a flowchart illustrating the process of the detection step inthe surface-defect detecting method as the third embodiment of thepresent invention. As illustrated in FIG. 7, first in the detection stepof the present embodiment, the image processing device 5 performs imagecorrection processing, such as calibration, shading correction, andnoise removal, using camera parameters derived in advance on each of twotwo-dimensional images (raw images “a” and “b”) input from the areasensors 4 a and 4 b, thereby generating a correction image “a” and acorrection image “b” (steps S21 a and S21 b). Through this imagecorrection processing, the following bright-portion extractionprocessing and the like can be accurately performed.

Subsequently, the image processing device 5 performs, on each of thecorrection images “a” and “b”, bright portion binarization processing ofdetecting a bright portion by setting one to the value of a pixel havinga luminance equal to or higher than a threshold value and setting zeroto the value of a pixel having a luminance lower than the thresholdvalue. Through this processing, the image processing device 5 generatesa bright-portion binarized image “a” and a bright-portion binarizedimage “b” (steps S22 a and S22 b). Subsequently, the image processingdevice 5 performs AND processing on the bright-portion binarized images“a” and “b”. Through this processing, the image processing device 5extracts a pixel having the value of one in both bright-portionbinarized images “a” and “b”, thereby generating a combinedbright-portion image (step S23). A set of pixels having the value of onein the combined bright-portion image form a bright-portion overlappingportion at which bright portions of the bright-portion binarized images“a” and “b” overlap. Accordingly, a portion at which high-reflectanceportions overlap in the bright-portion binarized images “a” and “b” canbe extracted. FIG. 8 (a) illustrates an exemplary bright-portionbinarized image “a”, FIG. 8 (b) illustrates an exemplary bright-portionbinarized image “b”, and FIG. 8 (c) illustrates an exemplary combinedbright-portion image.

Subsequently, the image processing device 5 labels pixels of a brightportion having the value of one in the generated combined bright-portionimage and sets each blob (set of labeled pixels) as a base-steel-portioncandidate portion. A white portion (which is a bright portion) in thecombined bright-portion image in FIG. 8 (c) is the base-steel-portioncandidate portion and corresponds to an overlapping portion of brightportions of the bright-portion binarized images “a” and “b”. The imageprocessing device 5 performs OR processing on the bright-portionbinarized images “a” and “b” generated at steps S22 a and S22 b thistime. Through this processing, the image processing device 5 generates abright-portion OR image. FIG. 8 (d) illustrates an exemplarybright-portion OR image. Then, the image processing device 5 labels theabove-described base-steel-portion candidate portion in thebright-portion OR image obtained through this processing. Then, theimage processing device 5 extracts, as a blob, each bunch of brightportions including the labeled base-steel-portion candidate portion.Each blob is set as a surface-defect candidate portion (step S24). FIG.8 (e) illustrates an exemplary surface-defect candidate portion. A whiteportion (which is a bright portion) is the surface-defect candidateportion. In the combined bright-portion image in FIG. 8 (c), a brightportion that is unlikely to be a surface defect is deleted through theAND processing at step S23. As clearly understood from FIG. 8 (d), allbright portions remain through the OR processing irrespective of whetherthey are defects. Thus, the surface-defect candidate portion can bereliably detected through labeling with the base-steel-portion candidateportion.

Subsequently, the image processing device 5 calculates, for eachsurface-defect candidate portion, as a combined-bright-portionoccupancy, a value obtained dividing the area of the base-steel-portioncandidate portion by the area of the surface-defect candidate portion(step S25). Lastly, the image processing device 5 detects a base steelportion by determining whether the surface-defect candidate portion is abase steel portion based on the calculated combined-bright-portionoccupancy. When having determined that the surface-defect candidateportion is not a base steel portion, the image processing device 5determines that the surface-defect candidate portion is a surface defect(step S26).

Note that a plurality of indexes are available for indicating the degreeof overlapping of the base-steel-portion candidate portion over thesurface-defect candidate portion. For example, the ratio at which thearea of each base-steel-portion candidate portion occupies the area ofthe surface-defect candidate portion of the two images before subjectedto the AND processing can be used. In this case, the area of thesurface-defect candidate portion is, for example, the area of a portionobtained through OR processing on two surface-defect candidate portions,or the sum or maximum value of the areas of surface-defect candidateportions. A surface-defect candidate portion is more likely to be a basesteel portion as its combined-bright-portion occupancy is higher. Thus,a certain threshold value may be used to determine that thesurface-defect candidate portion is a base steel portion when thecombined-bright-portion occupancy is equal to or larger than thethreshold value.

Alternatively, the combined-bright-portion occupancy may be used as acharacteristic amount, and a base steel portion may be detected by usinga well-known machine learning method such as a binary decision treemethod. Specifically, the combined-bright-portion occupancy and a resultof determination of whether a surface defect exists in the examinationtarget part are used as teacher data to generate a learning-completedmodel by machine learning, an input value of the learning-completedmodel being the combined-bright-portion occupancy calculated from two ormore images, an output value of the learning-completed model being thevalue of determination of whether a surface defect exists in theexamination target part corresponding to the two or more images. Thegenerated learning-completed model may be set as a surface defect model,the combined-bright-portion occupancy may be input to the surface-defectdetermination model, and a base steel portion may be detected bydetermining whether the surface-defect candidate portion is a base steelportion by using the output value of the surface-defect determinationmodel. Accordingly, it is possible to accurately distinguish a basesteel portion and a surface defect, which have been difficult todistinguish by a conventionally method.

Whether the surface-defect candidate portion is a base steel portion maybe determined based on the positional relation between thesurface-defect candidate portion and the base steel portion calculatedin the first to third embodiments. Specifically, the surface-defectcandidate portion is likely to be a base steel portion when thesurface-defect candidate portion and the base steel portion calculatedin the first to third embodiments are at substantially same positions.Similarly to the first to third embodiments, another processing methodis applicable when the degree of overlapping of bright portions in thesurface-defect candidate portion can be calculated.

Fourth Embodiment

When concave and convex portions slightly exist at a base steel portion,a bright-dark pattern similar to a concave-convex surface defect occurs,and the base steel portion and the concave-convex surface defect cannotbe accurately distinguished in some cases. Thus, in the presentembodiment, the base steel portion and the concave-convex surface defectare accurately distinguished by using, as a surface-defect candidateportion, a concave-convex surface defect that can be calculated by themethods disclosed in Patent Literatures 1 and 2. Subsequently, asurface-defect detecting method as the fourth embodiment of the presentinvention will be described below with reference to FIG. 9.

The surface-defect detecting method as the fourth embodiment of thepresent invention includes an irradiation step, an image capturing step,and a detection step. In the irradiation step, the light sources 2 a and2 b emit distinguishable illumination light beams L to a sameexamination target part on the surface of the steel pipe P in accordancewith a trigger signal from the function generator 3. In the imagecapturing step, the area sensors 4 a and 4 b capture, in accordance witha trigger signal from the function generator 3, two-dimensional imagesformed by reflected light beams of the illumination light beams Lemitted from the light sources 2 a and 2 b. In the detection step, theimage processing device 5 distinguishes scale and/or harmless pattern, aconcave-convex surface defect, and a base steel portion by using twotwo-dimensional images input from the area sensors 4 a and 4 b.

FIG. 9 is a flowchart illustrating the process of the detection step inthe surface-defect detecting method as the fourth embodiment of thepresent invention. As illustrated in FIG. 9, first in the detection stepof the present embodiment, the image processing device 5 performs imagecorrection processing, such as calibration, shading correction, andnoise removal, using camera parameters derived in advance on each of twotwo-dimensional images (raw images “a” and “b”) input from the areasensors 4 a and 4 b, thereby generating a correction image “a” and acorrection image “b” (steps S31 a and S31 b). Subsequently, the imageprocessing device 5 performs subtraction processing on the correctionimages “a” and “b”, thereby generating a subtraction image (step S32).Subsequently, the image processing device 5 calculates a concave-convexsurface-defect candidate portion in the examination target part based onthe generated subtraction image and outputs information related to scaleand/or harmless pattern removed through the subtraction processing (stepS33).

The image processing device 5 performs, on each of the correction images“a” and “b” generated at steps S31 a and S31 b, bright portionbinarization processing of detecting a bright portion by setting one tothe value of a pixel having a luminance equal to or higher than athreshold value and setting zero to the value of a pixel having aluminance lower than the threshold value. Through this processing, theimage processing device 5 generates a bright-portion binarized image “a”and a bright-portion binarized image “b” (steps S34 a and S34 b).Subsequently, the image processing device 5 performs AND processing onthe bright-portion binarized images “a” and “b”. Through thisprocessing, the image processing device 5 extracts a pixel having thevalue of one in both bright-portion binarized images “a” and “b”,thereby generating a combined bright-portion image (step S35).Subsequently, the image processing device 5 performs masking calculationprocessing on the combined bright-portion image by using theconcave-convex surface-defect candidate portion calculated through theprocessing at step S33. Through this processing, the image processingdevice 5 generates a cut-out combined bright-portion image by cuttingout a target region of determination as a base steel portion (step S36).Subsequently, the image processing device 5 calculates, as thecombined-bright-portion occupancy, the ratio at which the cut-outcombined bright-portion image occupies the entire concave-convexsurface-defect candidate portion (step S37). In addition, the imageprocessing device 5 determines whether the concave-convex surface-defectcandidate portion is a base steel portion or a concave-convex surfacedefect through threshold value processing or the like using thecalculated combined-bright-portion occupancy. When having determinedthat the concave-convex surface-defect candidate portion is not a basesteel portion, the image processing device 5 determines that theconcave-convex surface-defect candidate portion is a surface defect(step S38). The base steel portion leads to a bright signal across theconcave-convex surface-defect candidate portion for two irradiationdirections, but the concave-convex surface defect has shade and shadowthat are different between two irradiation directions, and thus brightand dark portions are generated at different positions across theconcave-convex surface-defect candidate portion. Accordingly, thecombined-bright-portion occupancy of the base steel portion is high, andthe combined-bright-portion occupancy of the concave-convex surfacedefect is low.

Note that a well-known method may be used as the method of detecting aconcave-convex surface-defect candidate portion in the examinationtarget part by using the subtraction image obtained through theprocessing at step S33. In particular, the methods disclosed in PatentLiteratures 1 and 2 can eliminate scale and/or harmless pattern throughsubtraction processing and use a unique bright-dark pattern generated bya concave-convex surface defect, and thus have such an advantage thatthe methods can accurately detect a concave-convex surface-defectcandidate portion, and accordingly, the methods are preferable. Thefollowing describes, as an example of the processing at step S33, anexample using the technologies disclosed in Patent Literatures 1 and 2.

When Ia(x, y) (the number of pixels is X×Y, the x coordinate satisfies1≤x≤X, and the y coordinate satisfies 1≤y≤Y) represents the luminancevalue of each pixel included in a two-dimensional image Ia obtained in acase in which the illumination light beam L is emitted from the lightsource 2 a, and Ib(x, y) represents the luminance value of each pixelincluded in a two-dimensional image Ib obtained in a case in which theillumination light beam L is emitted from the light source 2 b, theluminance value I_diff(x, y) of each pixel of a subtraction image I_diffthat can be obtained through the subtraction processing at step S32 isexpressed by Expression (1) described below.

I_diff(x,y)=I*,y)−Ib(x,y)  (1)

FIGS. 10 (a), (b), and (c) illustrate exemplary two-dimensional imagesIa and Ib obtained by capturing sound scale and/or harmless pattern,which are not concave-convex surface defects nor surface defects, and anexemplary subtraction image I_diff thereof, respectively. As illustratedin FIGS. 10 (a), (b), and (c), the angle between the normal vector ofthe surface and the light source 2 a is equal to the angle between thenormal vector of the surface and the light source 2 b irrespective ofthe existence of scale and/or harmless pattern at a sound portion, andthus the luminance value Ia(x, y)=the luminance value Ib(x, y), in otherwords, the luminance value I_diff(x, y)=0 holds.

However, the surface has a concave-convex shape at a concave-convexsurface defect portion, and thus there always exists a place where theangle between the normal vector of the surface and the light source 2 ais not equal to the angle between the normal vector of the surface andthe light source 2 b, and the luminance value Ia(x, y) # the luminancevalue Ib(x, y), in other words, the luminance value I_diff(x, y) #0holds. Thus, images of sound scale and/or harmless pattern, which arenot surface defects, can be removed by generating the subtraction imageI_diff of two two-dimensional images through subtraction processing by adifferentiator 11.

The following describes the logic of detection of a concave-convexsurface-defect candidate portion from the subtraction image I_diff.FIGS. 11 (a) and (b) are diagrams illustrating shade and shadow whenillumination light beam is emitted to the examination target part fromone of the light sources when the surface shape of the examinationtarget part is a concave shape and a convex shape, respectively. Asillustrated in FIG. 11 (a), when the surface shape of the examinationtarget part is a concave shape, the side closer to the light source isdark due to decrease of the quantity of irradiation light per unit area,and the side farther from the light source is bright due to approach tothe direction of regular reflection. However, when the surface shape ofthe examination target part is a convex shape as illustrated in FIG. 11(b), the side closer to the light source is bright due to approach tothe direction of regular reflection, and the side farther from the lightsource is dark in the shadow of the convex shape.

Specifically, the bright-dark pattern of reflected light beams ofillumination light beams differs, depending on whether the surface shapeof the examination target part is a concave shape or a convex shape.Thus, the existence of a concave-convex surface-defect candidate portioncan be detected by recognizing the bright-dark pattern of reflectedlight beam. Thus, next follows a description of a method of detecting aconcave-convex surface-defect candidate portion by recognizing thebright-dark pattern of reflected light beam. Note that a concavesurface-defect candidate portion in a concave-convex surface-defectcandidate portion is detected in the following description, but a convexsurface-defect candidate portion can be detected by the same logic. Abright portion in the following description means a blob having an areaequal to or larger than a predetermined value and obtained by performingcoupling processing on pixels having a luminance equal to or larger thana predetermined threshold value in the subtraction image I_diff. A darkportion in the following description means a blob having an area equalto or larger than a predetermined value and obtained by performingcoupling processing on pixels having a luminance equal to or smallerthan a predetermined threshold value in the subtraction image I_diff. Ablob means a set of labeled pixels.

Note that the above-described predetermined threshold values fordetecting bright and dark portions, and the above-describedpredetermined values for determining the areas are adjusted onconsideration of net detection performance. In other words, as thepredetermined threshold values and the predetermined values are set tobe larger, only a defect having a stronger signal can be detected, butan excessive detection signal due to surface roughness and noise can bereduced. Thus, adjustment is preferably performed to satisfy targetdetection performance and an allowed excessive detection degree based ondefinition of a harmful defect, usage, and distinguishment performanceof determining a defect from the bright-dark pattern.

In the present embodiment, the bright-dark pattern is recognized byextracting adjacent bright and dark portions through threshold valueprocessing.

Specifically, in the surface-defect detecting apparatus 1 illustrated inFIG. 1, since the light sources 2 a and 2 b are symmetrically disposedin the right-left direction with respect to the normal vector of theexamination target part, the bright-dark pattern of reflected lightattributable to the concave-convex shape of the surface is generated inthe right-left direction. The right-left direction of bright and darkportions is reserved depending on the order of subtraction processing,and thus in this example, a concave shape is defined to be a case inwhich the bright portion is on the right and the dark portion is on theleft, and a convex shape is defined to be a case in which the darkportion is on the right and the bright portion is on the left. Thus, thesubtraction image I_diff of a concave surface defect is as illustratedin FIG. 12. When images of the bright and dark portions are binarizedwith luminance threshold values “The” and “-The”, respectively,binarized images I_bright and I_dark of the bright and dark portions areexpressed by Expression (2) described below.

$\begin{matrix}\left. \begin{matrix}{{{I\_ bright}\left( {x,y} \right)} = {1\left( {{{when}\mspace{14mu}{I\_ diff}\left( {x,y} \right)} \geq {The}} \right)}} \\{{{I\_ bright}\left( {x,y} \right)} = {0\left( {{{when}\mspace{14mu}{I\_ diff}\left( {x,y} \right)} < {The}} \right)}} \\{{{I\_ dark}\left( {x,y} \right)} = {1\left( {{{when}\mspace{14mu}{I\_ diff}\left( {x,y} \right)} \leq {- {The}}} \right)}} \\{{{I\_ dark}\left( {x,y} \right)} = {0\left( {{{when}\mspace{14mu}{I\_ diff}\left( {x,y} \right)} > {- {The}}} \right)}}\end{matrix} \right\} & (2)\end{matrix}$

After the images of the bright and dark portions are binarized in thismanner and coupling and isolated-point removal are performed asnecessary, the positional relation between adjacent bright and darkportions is calculated to detect the existence of a concave-convexsurface defect. Note that various methods are available as the method ofcalculating the positional relation between adjacent bright and darkportions and three typical calculation methods are described below, butany other calculation method is applicable as long as the method cancalculate the positional relation between bright and dark portions.

The first positional relation calculation method calculates thepositional relation between adjacent bright and dark portions byperforming, on the adjacent bright and dark portions,expansion-contraction processing in a specific direction. FIG. 13illustrates a flowchart of the present calculation method. Since aconcave surface defect is detected in the present embodiment, thefollowing description is made on recognition of a bright-dark pattern inwhich the bright portion is on the right and the dark portion is on theleft. Since the bright portion is on the right and the dark portion ison the left, the dark portion always exists on the left side of thebright portion, and the bright portion always exists on the right sideof the dark portion. Thus, first in the present calculation method, theimage processing device 5 performs rightward expansion processing on thedark portion and performs leftward expansion processing on the brightportion (steps S331 a and S331 b). When I_bright_extend andI_dark_extend represent images of the bright and dark portions,respectively, subjected to the extension processing and W represents thelength of extension, the extension processing is expressed by Expression(3) described below. The origin is defined to be at the upper-leftcorner of each two-dimensional image, the positive y-axis direction isdefined to be the downward direction, and the positive x-axis directionis defined to be the right direction.

I_bright_extend(x1,y)=1 x−W≤x1≤x(when I_bright(x,y)=1)

I_dark_extend(x1,y)=1 x≤x1≤x+W(when I_dark(x,y)=1)  (3)

Note that although the bright and dark portions are extended by the samelength W in the present embodiment, the length W of extension does notnecessarily need to be same, and extension processing may be performedon only one of the bright and dark portions in an extreme case. Inaddition, the length W of extension depends on the size of a surfacedefect to be detected.

Subsequently, the image processing device 5 performs and processing onthe images I_bright_extend and I_dark_extend of the bright and darkportions subjected to extension processing as in Expression (4)described below, thereby extracting, as a concave defect candidateportion image I_defect, an overlapping portion of the imagesI_bright_extend and I_dark_extend of the bright and dark portionssubjected to extension processing (steps S332 a and S332 b).

I_defect=I_bright_extend & I_dark_extend  (4)

Subsequently, the image processing device 5 performs coupling andisolated-point removal processing on each obtained concave defectcandidate portion image I_defect as necessary, and then performslabeling processing to generate a concave defect candidate blobI_defect_blob (step S333). Then, the image processing device 5 extractsthe characteristic amount of each concave defect candidate blobI_defect_blob, and determines whether the concave defect candidate blobI_defect_blob is a concave surface defect based on a result of theextraction (steps S334 a and S334 b). Note that information of thebright and dark portions is needed to investigate the characteristicamount of the concave defect candidate blob I_defect_blob, and thus thebright and dark portions are restored from the concave defect candidateblob I_defect_blob.

Specifically, since the bright portion always exists on the right sideof a concave defect candidate portion and the dark portion always existson the left side thereof, the image processing device 5 searches thedark-portion binarized image I_dark to the left side with the barycenterof the concave defect candidate blob I_defect_blob as the startingpoint, and sets a blob found first as a dark-portion concave defectcandidate blob I_dark_blob. Similarly, the image processing device 5searches the bright-portion binarized image I_bright to the right withthe barycenter of the concave defect candidate blob I_defect_blob as thestarting point, and sets a blob found first as a bright-portion concavedefect candidate blob I_bright_blob. Then, the image processing device 5extracts a characteristic amount from the bright-portion concave defectcandidate blob I_bright_blob and the dark-portion concave defectcandidate blob I_dark_blob restored in this manner, and determineswhether each concave defect candidate blob I_defect_blob is a concavesurface defect based on the extracted characteristic amount. A specificcharacteristic amount differs depending on a defect, and thus is notdescribed here but is described later in an example.

A concave-convex surface-defect candidate portion used incombined-bright-portion occupancy calculation to be described later canbe calculated based on the restored bright-portion concave defectcandidate blob and dark-portion concave defect candidate blob.Specifically, the concave-convex surface-defect candidate portion iscalculated as a region obtained by calculating blobs of the bright anddark portions to acquire binarized images thereof and performing ORprocessing on the binarized images, or as a region obtained by furtherperforming extension-contraction processing to fill a portion betweenthe bright and dark portions. Note that although description is made ona concave defect candidate portion in the present embodiment forsimplification of description, the bright-dark pattern is reversed for aconvex defect candidate portion as illustrated in FIGS. 11 (a) and (b),and thus calculation can be performed through the same processing byreversing the positional relation between bright and dark portions inthe processing illustrated in FIG. 13.

Note that, in the above-described extension-contraction processing, asillustrated in FIG. 14, extension processing with a certain shape(replacement of pixels forming an outline portion of the target blob byusing a defined shape) is first performed on an image (FIG. 14 (d))obtained through AND processing on a dark-portion binarized image (FIG.14 (b)) and a bright-portion binarized image (FIG. 14 (c)) generatedfrom a raw image (FIG. 14 (a)), and thereafter, contraction processingwith a certain shape (deletion of pixels forming an outline portion ofthe target blob by using a defined shape) is performed to obtain animage (FIG. 14 (e)). As for an extension-contraction distance parameter,any shape is applicable as long as the blob is larger than the distancebetween the bright and dark portions, but the shape is desirably samebetween the extension processing and the contraction processing. Thesame result can be obtained by performing the extension-contractionprocessing a plurality of times for respective pixels.

In the second positional relation calculation method, after theabove-described threshold value processing is performed and coupling andisolated-point removal processing is performed as necessary, bright anddark portions are extracted and labeling is performed to recognize thepositional relation between adjacent bright and dark portions, therebydetecting a concave surface defect. Specifically, first, the imageprocessing device 5 individually recognizes bright and dark portionsthrough labeling and obtains barycenter information of the bright anddark portions. Subsequently, the image processing device 5 determineswhether the barycenter of a dark portion exists in a predetermined rangeon the right side of each bright portion based on the barycenterinformation of the bright and dark portions. When the barycenter of adark portion exists, the image processing device 5 recognizes the pairof bright and dark portions as a bright-dark pattern and performscharacteristic amount analysis on the bright-dark pattern to determinewhether the pair corresponds to a concave surface defect. Note thatalthough the bright-dark pattern is recognized by using the barycenterinformation in this example, information used to recognize thebright-dark pattern does not necessarily need to be the barycenterinformation as long as the information is information (for example, anupper-end position or a lower-end position) with which the positions ofthe adjacent bright and dark portions can be understood. Note thatalthough description is made on a concave defect candidate portion inthe present embodiment for simplification of description, thebright-dark pattern is reversed for a convex defect candidate portion asillustrated in FIGS. 11 (a) and (b), and thus calculation can beperformed through the same processing by reserving the positionalrelation between bright and dark portions in the processing illustratedin FIG. 13.

In the third positional relation calculation method, the above-describedthreshold value processing is not performed, and a concave surfacedefect is detected by recognizing a bright-dark pattern by using afilter. Specifically, in the surface-defect detecting apparatus 1illustrated in FIG. 1, since the light sources 2 a and 2 b aresymmetrically disposed in the right-left direction with respect to thenormal of the examination target part, a bright-dark patternattributable to concave and convex portions of the surface is generatedin the right-left direction. FIGS. 15 (a) and (b) are a diagramillustrating an exemplary subtraction image and a diagram illustratingthe one-dimensional profile of a bright-dark pattern along line segmentL illustrated in FIG. 15 (a), respectively.

As illustrated in FIGS. 15 (a) and (b), since the bright portion is onthe right and the dark portion is on the left in a concave surfacedefect, the one-dimensional profile of the bright-dark pattern is acharacteristic one-dimensional profile having a mountain shape on theright side and a valley shape on the left side. Thus, in the presentembodiment, a filter H for a mountain shape on the right side and avalley shape on the left side is produced in advance and applied to thesubtraction image I_diff as indicated in Expression (5) below, therebygenerating a two-dimensional image I_cont in which high-frequency noiseis reduced and only the bright-dark pattern is enhanced.

I_cont=H*I_diff  (5)

FIGS. 16 (a) and (b) are a diagram illustrating a two-dimensional imageof the filter H produced in advance and a diagram illustrating anexemplary one-dimensional profile thereof in the right-left direction,respectively. FIGS. 17 (a) and (b) are a diagram illustrating asubtraction image subjected to filter processing using the filter Hillustrated in FIGS. 16 (a) and (b) and a diagram illustrating theone-dimensional profile thereof in the right-left direction,respectively. As illustrated in FIGS. 17 (a) and (b), a two-dimensionalimage in which high-frequency noise is reduced and only the bright-darkpattern is enhanced is obtained. Note that although description is madeon a concave defect candidate portion in the present embodiment forsimplification of description, the bright-dark pattern is reversed for aconvex defect candidate portion as illustrated in FIGS. 11 (a) and (b),and thus, calculation can be performed through the same processing byreversing the shape of the filter.

Note that several kinds of filters having ranges different from eachother in a width direction may be prepared as necessary to support alarge number of surface defect sizes. The image processing device 5performs coupling and isolated-point removal processing as necessary onthe two-dimensional image in which the bright-dark pattern is enhancedin this manner, and then performs threshold value processing to extracta defect candidate portion image I_defect. Then, the image processingdevice 5 detects a concave surface defect by performing, on theextracted defect candidate portion image I_defect, processing same asthat of the first positional relation calculation method. Note that ascale or harmless pattern, which has the same appearance between twoimages before the subtraction and forms no bright-dark pattern after thesubtraction, can be extracted by excluding a candidate portion thatforms a bright-dark pattern after the subtraction from amongconcave-convex surface-defect candidate portions extracted by binarizingand labeling the images before the subtraction.

Example

FIG. 18 illustrates an exemplary histogram of thecombined-bright-portion occupancy calculated in an actual-machine testby using the surface-defect detecting method according to the fourthembodiment and a surface-defect apparatus 1. In FIG. 18, the horizontalaxis represents the combined-bright-portion occupancy in percentage, andthe vertical axis represents the frequency of a base steel portion or aconcave-convex surface defect in percentage. In FIG. 18, each blackrhombus indicates how much 100% of places visually checked as a “basesteel portion” are distributed to the correspondingcombined-bright-portion occupancy. In FIG. 18, each white rectangleindicates how much 100% of places visually checked as a “concave-convexsurface defect” are distributed to the correspondingcombined-bright-portion occupancy. Note that in the present example, abright portion was defined to be a portion having a luminance more than1.5 times higher than that of a sound portion. A concave-convexsurface-defect candidate portion was detected by using bright-darkpattern detection and extension-contraction processing.

As illustrated in FIG. 18, it can be checked that the concave-convexsurface defect and the base steel portion are extremely well separatedat a threshold value at which the combined-bright-portion occupancy is30% approximately. In a case of an examination in which the base steelportion is excessively detected, a concave-convex surface-defectcandidate portion having a combined-bright-portion occupancy of, forexample, 30% or higher is determined as the base steel portion. Withthis method, 95% or more of the base steel portion can be removedwithout excluding the concave-convex surface defect at all. Note thatthe method of performing distinguishment by directly using thecombined-bright-portion occupancy as the threshold value is effective,but the same result can be obtained through distinguishment usingmachine learning with the combined-bright-portion occupancy as onecharacteristic amount.

Fifth Embodiment

Lastly, a surface-defect detecting method as the fifth embodiment of thepresent invention will be described below with reference to FIG. 19.

The surface-defect detecting method as the fifth embodiment of thepresent invention includes an irradiation step, an image capturing step,and a detection step. In the irradiation step, the light sources 2 a and2 b emit distinguishable illumination light beams L to a sameexamination target part on the surface of the steel pipe P in accordancewith a trigger signal from the function generator 3. In the imagecapturing step, the area sensors 4 a and 4 b capture, in accordance witha trigger signal from the function generator 3, two-dimensional imagesformed by reflected light beams of the illumination light beams Lemitted from the light sources 2 a and 2 b. In the detection step, theimage processing device 5 distinguishes scale and/or harmless pattern, aconcave-convex surface defect, and a base steel portion by using twotwo-dimensional images input from the area sensors 4 a and 4 b.

FIG. 19 is a flowchart illustrating the process of the detection step inthe surface-defect detecting method as the fifth embodiment of thepresent invention. As illustrated in FIG. 19, first in the detectionstep of the present embodiment, the image processing device 5 performsimage correction processing, such as calibration, shading correction,and noise removal, using camera parameters derived in advance on each oftwo two-dimensional images (raw images “a” and “b”) input from the areasensors 4 a and 4 b, thereby generating a correction image “a” and acorrection image “b” (steps S41 a and S41 b). Subsequently, the imageprocessing device 5 performs subtraction processing on the correctionimages “a” and “b”, thereby generating a subtraction image (step S42). Aconcave-convex surface-defect candidate portion in the examinationtarget part is calculated based on the generated subtraction image (stepS43). Specifically, the image processing device 5 calculates aconcave-convex surface-defect candidate portion in the examinationtarget part based on the generated subtraction image and outputsinformation related to scale and/or harmless pattern removed through thesubtraction processing.

Subsequently, the image processing device 5 performs, on each of twotwo-dimensional images (raw images “a” and “b”) input from the areasensors 4 a and 4 b, masking calculation processing using theconcave-convex surface-defect candidate portion calculated at theprocessing at step S43. Through this processing, the image processingdevice 5 generates a cut-out raw image “a” and a cut-out raw image “b”obtained by cutting out a target region of determination as a base steelportion (steps S44 a and S44 b). Subsequently, the image processingdevice 5 performs image correction processing on the cut-out raw images“a” and “b”, thereby generating a cut-out correction image “a” and acut-out correction image “b” (steps S45 a and S45 b).

Subsequently, the image processing device 5 performs, on each of thecut-out correction images “a” and “b”, bright portion binarizationprocessing of detecting a bright portion by setting one to the value ofa pixel having a luminance equal to or higher than a threshold value andsetting zero to the value of a pixel having a luminance lower than thethreshold value. Through this processing, the image processing device 5generates a cut-out bright-portion binarized image “a” and a cut-outbright-portion binarized image “b” (steps S46 a and S46 b).Subsequently, the image processing device 5 performs AND processing onthe cut-out bright-portion binarized images “a” and “b”. Through thisprocessing, the image processing device 5 extracts a pixel having thevalue of one in both cut-out bright-portion binarized images “a” and“b”, thereby generating a cut-out combined bright-portion image (stepS47). Subsequently, the image processing device 5 calculates, as thecombined-bright-portion occupancy, the ratio at which the cut-outcombined bright-portion image occupies the entire concave-convexsurface-defect candidate portion (step S48). In addition, the imageprocessing device 5 determines whether the concave-convex surface-defectcandidate portion is a base steel portion or a concave-convex surfacedefect through threshold value processing or the like using thecalculated combined-bright-portion occupancy. When having determinedthat the concave-convex surface-defect candidate portion is not a basesteel portion, the image processing device 5 determines that theconcave-convex surface-defect candidate portion is a surface defect(step S49).

Note that a well-known method may be used as the method of detecting aconcave-convex surface-defect candidate portion in the examinationtarget part by using the subtraction image obtained through theprocessing at step S43. In particular, the methods disclosed in PatentLiteratures 1 and 2 can eliminate scale and/or harmless pattern throughsubtraction processing and use a unique bright-dark pattern generated bya concave-convex surface defect, and thus have such an advantage thatthe methods can accurately detect a concave-convex surface-defectcandidate portion, and accordingly, the methods are preferable. Theabove-described processing at step S33 may be exemplary processing atstep S43.

Although each embodiment to which the invention achieved by theinventors is applied is described above, the present invention is notlimited by description and drawings as parts of the disclosure of thepresent invention according to the present embodiment. For example,although the light sources 2 a and 2 b are symmetrically installed inthe right-left direction and thus a bright-dark pattern in theright-left direction is recognized in the present embodiment, aconcave-convex surface defect can be detected through the sameprocessing when the installation positions of the light sources 2 a and2 b are symmetric in the up-down direction instead of the right-leftdirection or are not symmetric. Specifically, when the light sources aresymmetrically disposed in the up-down direction, the bright-dark patternonly changes from the right-left direction to the up-down direction, andthus a concave-convex surface defect can be detected through the sameprocessing by rotating the bright-dark pattern by 90°.

When the light sources 2 a and 2 b are installed so that the irradiationdirections of illumination light beams are different from each other by90° as illustrated in FIG. 20, the side closer to a light source is darkand the side farther from the light source is bright for a concavesurface defect, or the side closer to the light source is bright and theside farther from the light source is dark for a convex surface defect.Specifically, in the case of a concave surface defect, a two-dimensionalimage obtained by illumination light beam from the light source 2 a isas illustrated in FIG. 21 (a), and a two-dimensional image obtained byillumination light beam from the light source 2 b is as illustrated inFIG. 21 (b). Accordingly, their subtraction image is a bright-darkpattern having contrast from the lower-left corner to the upper-rightcorner as illustrated in FIG. 21 (c). Thus, when the bright-dark patternis rotated by 45°, the concave surface defect can be detected by amethod same as that for a bright-dark pattern in the right-leftdirection. When three or more light sources are used, a subtractionimage of a plurality of patterns can be obtained, and thus the accuracyof surface-defect detection can be further improved.

In the present embodiment, a concave-convex surface defect is detectedwhen illumination light beams is irradiated in directions symmetric withrespect to the normal of an examination target part, but the irradiationdirections of illumination light beams do not necessarily need to besymmetric. The surface-defect detecting method of the present embodimentis applicable to any steel-material production line irrespective of hotrolling and cold rolling. When high-reflectance marking is applied tothe product surface for product distinguishment, as well, only a markingportion can be detected by the same method. The marking has a uniqueshape in many cases, and thus can be distinguished by using a typicalimage characteristic amount.

When a steel material is manufactured while a surface defect of thesteel material is detected by using the surface-defect detectingapparatus 1 or the surface-defect detecting method as an embodiment ofthe present invention, a base steel portion and a harmful surface defectcan be accurately distinguished to improve the manufacturing yield ofthe steel material. For example, a cold-rolling scratch is one of mosttypical base steel portions in an iron steel process. A surface defect(concave-convex surface defect, in particular) is typically generated bypressing of a protrusion such as a roll during hot rolling, and acold-rolling scratch is typically generated by surface rubbing duringconveyance. Thus, when these defects can be distinguished and detected,a defect generation factor can be easily specified. As a result, afactor on a production line can be promptly removed to prevent furthergeneration. The surface-defect detecting apparatus 1 as an embodiment ofthe present invention may be applied as an examination device includedin a steel-material manufacturing facility. Specifically, a steelmaterial manufactured by the manufacturing facility by using thesurface-defect detecting apparatus according to the present invention isexamined to detect a surface defect of the steel material. For theabove-described reason in this case as well, a base steel portion and aharmful surface defect can be accurately distinguished to improve themanufacturing yield of the steel material.

The quality of a steel material can be managed by classifying the steelmaterial based on the existence of a surface defect by using thesurface-defect detecting apparatus 1 or the surface-defect detectingmethod as an embodiment of the present invention. In other words, a basesteel portion and a harmful surface defect (concave-convex surfacedefect, in particular) can be accurately distinguished to improve themanufacturing yield of the steel material. For example, a cold-rollingscratch is one of most typical base steel portions in an iron steelprocess. When a concave-convex surface defect is harmful and acold-rolling scratch is harmless, false sensing of the cold-rollingscratch leads to determination that a sound steel material has a defect,and reduces the manufacturing yield in some cases. When the degree ofseriousness is different between a concave-convex surface defect and acold-rolling scratch in this manner, distinguishment and detection ofthese defects allow determination in accordance with needs forexamination of a steel material, thereby preventing decrease of themanufacturing yield of the steel material. In this manner, for example,other embodiments, examples, and applied technologies achieved by theskilled person in the art or the like based on the present embodimentare all included in the scope of the present invention.

INDUSTRIAL APPLICABILITY

According to the present invention, it is possible to provide asurface-defect detecting method, a surface-defect detecting apparatus, asurface-defect determination model generating method, and asurface-defect determination model that are capable of accuratelydistinguishing a base steel portion and a surface defect. In addition,according to the present invention, it is possible to provide asteel-material manufacturing method, a steel-material quality managementmethod, and a steel-material manufacturing facility that are capable ofimproving a manufacturing yield of a steel material by accuratelydistinguishing a base steel portion and a surface defect.

REFERENCE SIGNS LIST

-   -   1 surface-defect detecting apparatus    -   2 a, 2 b light source    -   3 function generator    -   4 a, 4 b area sensor    -   5 image processing device    -   6 monitor    -   L illumination light beam    -   P steel pipe

1-10. (canceled)
 11. A surface-defect detecting method of opticallydetecting a surface defect of a steel material, the method comprising:an irradiation step of irradiating an examination target part withillumination light beams from different directions by using two or moredistinguishable light sources; and a detection step of detecting asurface defect in the examination target part based on the degree ofoverlapping of bright portions extracted from two or more images formedby reflected light beams of the illumination light beams.
 12. Thesurface-defect detecting method according to claim 11, wherein thedegree of overlapping of the bright portions is a ratio at which anoverlapping portion of the bright portions occupies a surface-defectcandidate portion in the examination target part.
 13. The surface-defectdetecting method according to claim 11, wherein the detection stepincludes a step of calculating a surface-defect candidate portion in theexamination target part based on the bright portions extracted from twoor more images formed by reflected light beams of the illumination lightbeams, and a step of detecting a surface defect in the examinationtarget part based on a ratio at which an overlapping portion of thebright portions occupies the surface-defect candidate portion.
 14. Thesurface-defect detecting method according to claim 11, wherein thedetection step includes a step of detecting a surface defect in theexamination target part by using a surface-defect determination modelsubjected to machine learning such that a determination value indicatingwhether or not the surface defect exists in the examination target partcorresponding to the two or more images is output, when the degree ofoverlapping of bright portions extracted from the two or more images isinput.
 15. The surface-defect detecting method according to claim 12,wherein the detection step includes a step of detecting a surface defectin the examination target part by using a surface-defect determinationmodel subjected to machine learning such that a determination valueindicating whether or not the surface defect exists in the examinationtarget part corresponding to the two or more images is output, when thedegree of overlapping of bright portions extracted from the two or moreimages is input.
 16. The surface-defect detecting method according toclaim 13, wherein the detection step includes a step of detecting asurface defect in the examination target part by using a surface-defectdetermination model subjected to machine learning such that adetermination value indicating whether or not the surface defect existsin the examination target part corresponding to the two or more imagesis output, when the degree of overlapping of bright portions extractedfrom the two or more images is input.
 17. A steel-material manufacturingmethod comprising a step of manufacturing a steel material whiledetecting a surface defect of the steel material by using asurface-defect detecting method of optically detecting the surfacedefect of the steel material, the surface-defect detecting methodincluding: an irradiation step of irradiating an examination target partwith illumination light beams from different directions by using two ormore distinguishable light sources; and a detection step of detecting asurface defect in the examination target part based on the degree ofoverlapping of bright portions extracted from two or more images formedby reflected light beams of the illumination light beams.
 18. Thesteel-material manufacturing method according to claim 17, wherein thedegree of overlapping of the bright portions is a ratio at which anoverlapping portion of the bright portions occupies a surface-defectcandidate portion in the examination target part.
 19. The steel-materialmanufacturing method according to claim 17, wherein the detection stepincludes a step of calculating a surface-defect candidate portion in theexamination target part based on the bright portions extracted from twoor more images formed by reflected light beams of the illumination lightbeams, and a step of detecting a surface defect in the examinationtarget part based on a ratio at which an overlapping portion of thebright portions occupies the surface-defect candidate portion.
 20. Thesteel-material manufacturing method according to claim 17, wherein thedetection step includes a step of detecting a surface defect in theexamination target part by using a surface-defect determination modelsubjected to machine learning such that a determination value indicatingwhether or not the surface defect exists in the examination target partcorresponding to the two or more images is output, when the degree ofoverlapping of bright portions extracted from the two or more images isinput.
 21. The steel-material manufacturing method according to claim18, wherein the detection step includes a step of detecting a surfacedefect in the examination target part by using a surface-defectdetermination model subjected to machine learning such that adetermination value indicating whether or not the surface defect existsin the examination target part corresponding to the two or more imagesis output, when the degree of overlapping of bright portions extractedfrom the two or more images is input.
 22. steel-material manufacturingmethod according to claim 19, wherein the detection step includes a stepof detecting a surface defect in the examination target part by using asurface-defect determination model subjected to machine learning suchthat a determination value indicating whether or not the surface defectexists in the examination target part corresponding to the two or moreimages is output, when the degree of overlapping of bright portionsextracted from the two or more images is input.
 23. A steel-materialquality management method comprising a step of managing the quality of asteel material by classifying the steel material based on existence of asurface defect by using a surface-defect detecting method of opticallydetecting the surface defect of the steel material, the surface-defectdetecting method including: an irradiation step of irradiating anexamination target part with illumination light beams from differentdirections by using two or more distinguishable light sources; and adetection step of detecting a surface defect in the examination targetpart based on the degree of overlapping of bright portions extractedfrom two or more images formed by reflected light beams of theillumination light beams.
 24. steel-material quality management methodaccording to claim 23, wherein the degree of overlapping of the brightportions is a ratio at which an overlapping portion of the brightportions occupies a surface-defect candidate portion in the examinationtarget part.
 25. The steel-material quality management method accordingto claim 23, wherein the detection step includes a step of calculating asurface-defect candidate portion in the examination target part based onthe bright portions extracted from two or more images formed byreflected light beams of the illumination light beams, and a step ofdetecting a surface defect in the examination target part based on aratio at which an overlapping portion of the bright portions occupiesthe surface-defect candidate portion.
 26. The steel-material qualitymanagement method according to claim 23, wherein the detection stepincludes a step of detecting a surface defect in the examination targetpart by using a surface-defect determination model subjected to machinelearning such that a determination value indicating whether or not thesurface defect exists in the examination target part corresponding tothe two or more images is output, when the degree of overlapping ofbright portions extracted from the two or more images is input.
 27. Thesteel-material quality management method according to claim 24, whereinthe detection step includes a step of detecting a surface defect in theexamination target part by using a surface-defect determination modelsubjected to machine learning such that a determination value indicatingwhether or not the surface defect exists in the examination target partcorresponding to the two or more images is output, when the degree ofoverlapping of bright portions extracted from the two or more images isinput.
 28. The steel-material quality management method according toclaim 25, wherein the detection step includes a step of detecting asurface defect in the examination target part by using a surface-defectdetermination model subjected to machine learning such that adetermination value indicating whether or not the surface defect existsin the examination target part corresponding to the two or more imagesis output, when the degree of overlapping of bright portions extractedfrom the two or more images is input.
 29. A surface-defect detectingapparatus configured to optically detect a surface defect of a steelmaterial, the apparatus comprising: an irradiation unit configured toirradiate an examination target part with illumination light beams fromdifferent directions by using two or more distinguishable light sources;and a detection unit configured to detect a surface defect in theexamination target part based on the degree of overlapping of brightportions extracted from two or more images formed by reflected lightbeams of the illumination light beams.
 30. A steel-materialmanufacturing facility comprising: a manufacturing facility configuredto manufacture a steel material; and the surface-defect detectingapparatus according to claim 29 that is configured to examine the steelmaterial manufactured by the manufacturing facility.
 31. surface-defectdetermination model generating method, comprising: using the degree ofoverlapping of bright portions extracted from two or more images formedby reflected light beams of illumination light beams with which anexamination target portion is irradiated from different directions byusing two or more distinguishable light sources and a result ofdetermination of whether or not a surface defect exists in theexamination target portion, as teacher data; and generating alearning-completed model by machine learning as a surface-defectdetermination model, where an input value of the learning-completedmodel being the degree of overlapping of bright portions extracted fromthe two or more images and an output value of the learning-completedmodel being a value of determination of whether or not a surface defectexists in the examination target portion corresponding to the two ormore images.
 32. A surface-defect determination model generated by thesurface-defect determination model generating method according to claim31.